Ahh and I'd forgotten that 1574 included support in grid search. I should
perhaps take a look at that.
On Sun, Jun 2, 2013 at 1:10 AM, Andreas Mueller <amuel...@ais.uni-bonn.de>wrote:
> On 06/01/2013 01:03 PM, Joel Nothman wrote:
> > I haven't seen any patch for this precisely, though it's a known issue
> > (even if it doesn't seem to be explicitly ticketed; it's closest to
> > https://github.com/scikit-learn/scikit-learn/issues/1179). There are
> > various tricky cases not currently supported for which it's easiest to
> > roll your own search and/or pipeline infrastructures.
> >
> > Part of the issue is that presumably (or perhaps not?) your search
> > objective function needs to adjust for weights as well as the training
> > procedure, and currently our metrics don't support that (work in
> > progress at https://github.com/scikit-learn/scikit-learn/pull/1574).
> This is the one I meant.
>
> >
> > After that's in place there's still an API issue to apply this
> > generally: if `BaseSearchCV.fit` were modified to take extra arguments
> > that needed to be split in cross-validation, when are they passed to
> > `fit` and when to `score`? Perhaps that's not so hard if we're only
> > considering sample_weight. (And if Pipeline were modified to support
> > sample_weight, should it presume it to pass sample_weight to *all* its
> > transformers? to those accepting the parameter? to none but the last
> > estimator?)
> Questions like that are what made me suggest to split 1574 from the
> AdaBoost pr ;)
>
> Cheers,
> Andy
>
>
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